import torch
import torchvision
from torch.nn import Conv2d
from torch.utils.data import DataLoader
from torch import nn
from torch.utils.tensorboard import SummaryWriter

dataset = torchvision.datasets.CIFAR10("../../dataSet",
                                       train= False,
                                       transform=torchvision.transforms.ToTensor(),
                                       download=True)
dataloader = DataLoader(dataset ,batch_size=64, drop_last=False)


class Ah (nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = Conv2d(in_channels = 3,
                            out_channels = 6,
                            kernel_size = 3,
                            stride = 1,
                            padding = 0)

    def forward(self,x):
        x = self.conv1(x)
        return x

ah = Ah()
print(ah)

writer = SummaryWriter("../logs")
step = 0
for data in dataloader:
    imgs , targets = data
    output = ah(imgs)
    print(output.shape)
    writer.add_images("input",imgs,step)
    # 6个channels 不
    output = torch.reshape(output,(-1,3,30,30))
    writer.add_images("output",output,step)
    step +=1


writer.close()